Zobrazeno 1 - 10
of 118
pro vyhledávání: '"Nalpantidis, Lazaros"'
In precision agriculture, vision models often struggle with new, unseen fields where crops and weeds have been influenced by external factors, resulting in compositions and appearances that differ from the learned distribution. This paper aims to ada
Externí odkaz:
http://arxiv.org/abs/2410.23906
Autor:
Gregorek, Jakub, Nalpantidis, Lazaros
Even if the depth maps captured by RGB-D sensors deployed in real environments are often characterized by large areas missing valid depth measurements, the vast majority of depth completion methods still assumes depth values covering all areas of the
Externí odkaz:
http://arxiv.org/abs/2409.10202
Autor:
Schmidt, Patrick, Nalpantidis, Lazaros
The construction industry has been traditionally slow in adopting digital technologies. However, these are becoming increasingly necessary due to a plentitude of challenges, such as a shortage of skilled labor and decreasing productivity levels compa
Externí odkaz:
http://arxiv.org/abs/2407.09372
Laboratory processes involving small volumes of solutions and active ingredients are often performed manually due to challenges in automation, such as high initial costs, semi-structured environments and protocol variability. In this work, we develop
Externí odkaz:
http://arxiv.org/abs/2404.16529
Robot perception is far from what humans are capable of. Humans do not only have a complex semantic scene understanding but also extract fine-grained intra-object properties for the salient ones. When humans look at plants, they naturally perceive th
Externí odkaz:
http://arxiv.org/abs/2312.08805
We explore the use of uncertainty estimation in the maritime domain, showing the efficacy on toy datasets (CIFAR10) and proving it on an in-house dataset, SHIPS. We present a method joining the intra-class uncertainty achieved using Monte Carlo Dropo
Externí odkaz:
http://arxiv.org/abs/2307.01325
Autor:
Rudolph, Michael, Dawoud, Youssef, Güldenring, Ronja, Nalpantidis, Lazaros, Belagiannis, Vasileios
We present a lightweight encoder-decoder architecture for monocular depth estimation, specifically designed for embedded platforms. Our main contribution is the Guided Upsampling Block (GUB) for building the decoder of our model. Motivated by the con
Externí odkaz:
http://arxiv.org/abs/2203.04206
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021, pp. 954-962
Real-world perception systems in many cases build on hardware with limited resources to adhere to cost and power limitations of their carrying system. Deploying deep neural networks on resource-constrained hardware became possible with model compress
Externí odkaz:
http://arxiv.org/abs/2108.02671
Autor:
Yang, Wenzhen, Crone, Johan K., Lønkjær, Claus R., Ribo, Macarena Mendez, Shan, Shuo, Frumosu, Flavia Dalia, Papageorgiou, Dimitrios, Liu, Yu, Nalpantidis, Lazaros, Zhang, Yang
Publikováno v:
Journal of Intelligent Manufacturing and Special Equipment, 2023, Vol. 4, Issue 2, pp. 85-98.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JIMSE-01-2023-0001
